Model Constant Optimization Using an Ensemble of Experimental Data 2012-01-0131

Given the complexities of simulating fuel injection, most models contain empirical parameters. This work presents a method of automatically adjusting empirical model parameters in a computational fluid dynamics (CFD) sub-model in order to best agree with an ensemble of experimental measurements. The method is demonstrated by a multiphase flow simulation of flash-boiling fuel injector nozzles. This paper describes a framework to automatically set inputs, launch individual runs, read the output of these runs, and intelligently choose new input values based on the difference between calculated mass flow rates and experimental values, in order to minimize error. The Hooke-Jeeves search algorithm was chosen for the optimization process, since it is reasonably efficient, does not require calculating derivatives, and is robust. The scheme scales well when employed on computer clusters, where numerous calculations can be run simultaneously using a batch queuing system. The tuning process resulted in a set of constants that produce an average error of about 0.6% when compared to the ensemble of experimental data, a 77.5% reduction in error when compared to the performance of the original model parameters. This methodology is extensible, providing a general method for tuning CFD sub-models.